2 Project Description Automatic beat detection algorithms hae many clinical applications including pulse oximetry, cardiac arrhythmia detection, and cardiac output monitoring. Most of these algorithms have been developed by medical device companies and are proprietary. Thus, researchers who wish to investigate pulse contour analysis must rely on manual annotations or develop their own algorithms . The objoctive of this project is to design an automatic detection algorithm for intracranial pressure (ICP) signals that locates the first peak following each heart beat. This is called the percussion peak in ICP . Development of automatic detection algorithms is an active area of research. 3 Significance . The unavailability of robust detection algorithms for pressure signals has, at least partially, prevented re searchers from fully conducting beat-by-beat analysis. Current methods of intracranial ICP signal analysis are primarily based on time- or frequency-domain metrics such as mean standard deviation, and spectral power at the heart rate frequency. Few nvestigators have analyzed variations in the beat-level morphol- ogy of the pressure signals because detection algorithms that can automatically identify each of the beat components are generally unavailable. Many researchers manually annotate desired components of physiologic pressure signals because detection algorithms for these signals are not widely available. This approach is labor-intensive, subjective, expensive, and can only be used on short signal segments . There are numerous current and potential applications for pressure beat detection algorithms. Many pulse oximeters perform beat detection as part of the signal processing necessary to estimate oxygen saturation but these algorithms are proprietary and cannot be used in other applications. Systolic peak detection is necessary for some measures of baroreflex sensitivity. Identification of the pressure components is necessary for some methods that assess the interaction between respiration and beat-by-beat ventricular parameters and the modulation effects of respiration on left ventricular size and stroke volume. Detection is a necessary task when analyzing arterial compliance and the pressure pulse ontour. Beat-to-beat morphology analysis of ICP also requires robust automatic detection 4 Specifications . Design a DSP system to perform automatic detection of ICP signals. Function Structure:fi ressureDetect(x.fs.pf) Input signal Signal sample rate (Hz). Default = 125 Plot flag: 0=none (default), l=screen Percussion peak (PI) index, samples